GitHub29d
Gaussian Process and SVM on MNIST Data
This project consists of two main parts: implementing Gaussian Process Regression ... tools like matplotlib. SVM models are used to classify digits (0–4) from the MNIST dataset with comparisons across ...
eLife28d
Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel
The Gaussian kernels had higher risks of large errors ... including kernel ridge regression, support vector machines (SVM), kernel fisher discriminant analysis (k-FDA), kernel logistic regression (KLR ...
GitHub28d
Geographical Gaussian Process Regression
Geographical Gaussian Process Regression (GGPR): A Spatial Machine Learning ... By comparing the ability of traditional machine learning models (GPR, XGBoost, RF, SVM) presented in Ziqi Li (2024) to ...
IEEE15d
Sparsity-Aware Distributed Learning for Gaussian Processes With Linear Multiple Kernel
Abstract: Gaussian processes (GPs) stand as crucial tools in machine learning and signal processing, with their effectiveness hinging on kernel design and hyperparameter optimization. This article ...
Frontiers14d
Approximation properties relative to continuous scale space for hybrid discretisations of Gaussian derivative operators
Specifically, we will perform comparisons to the other main types of discretisation methods considered in (Lindeberg, 2024b), based on either (i) explicit convolutions with sampled Gaussian derivative ...
IEEE15d
Variational Mode Feature Construction-Based Improved Kernel Extreme Learning Machine for Rotating Machinery Intelligent Diagnosis
To deal with these issues, this paper proposes a variational mode feature construction-based improved kernel extreme learning machine (VMF-IKELM) for bearing and gear fault identification. The ...